『Supply ChAInge』のカバーアート

Supply ChAInge

Supply ChAInge

著者： Derek Aranda
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Join supply chain expert Derek Aranda for a series investigating the challenges and opportunities of cognitive supply chains. We interview enterprise-level experts in AI, risk, change management, ethics, HR, and logistics to gain an understanding of the real-life implementation and impact behind intelligent supply chain management. Like all good investigations, our conclusion is not a foregone one, as complex systems require complex analysis, and the cutting edge of technology, well, it can be sharp.Join us on Supply ChAInge to ask the question behind the question: not just can a cognitive supply chain work, but what does it take at scale? 経済学
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  • Agentic AI: Every AI Project Is a Data Project with Traey Hatch

    Agentic AI: Every AI Project Is a Data Project with Traey Hatch
    2026/08/28
    Traey Hatch of New Math Data on agentic AI: why every AI project is really a data project, and what agents need before they can run in production. The conversation covers the terminology blur between generative, physical, and agentic AI, the context work that decides whether a model is useful inside a specific enterprise, the cost logic behind frontier versus specialist models, and the identity, permissions, and audit gaps still keeping agents-supervising-agents out of production. 🎧 Episode Highlights [2:22] Traey's path from data warehousing and machine learning into a full AI practice, and why nearly every client conversation has shifted. [4:14] Reframing the McKinsey and MIT numbers on stalled AI projects. This is an experimental phase, not a failure phase. [6:45] The word AI now absorbs machine learning, workflow automation, and analytics. What that blur costs organizations trying to scope work. [10:51] Provenance as the adoption gate. People abandon a tool the first time it embarrasses them in front of someone else. [15:43] What a harness actually is, and why the hard part is structuring context rather than picking a model. [19:19] SAP means something different in every enterprise. Why tribal context has to be handed to a model explicitly. [23:26] Onboarding an agent the way you onboard an employee: credentials, permissions, roles, and where the data actually lives. [24:44] Choosing between frontier, specialist, and open source models as a cost decision tied to what the process is worth. [28:55] The shoebox story. Fifteen years of uncashed checks in a permit office, and what it says about process nobody designed. [33:34] The principal consultant approach. A negative proof of concept is a valid result, and off-ramps belong in the contract. [37:37] The age of the operations person, and why process fluency is the durable advantage. [39:37] Agents managing agents. Identity, permissions, and audit trails are the gaps, not model capability. [43:15] The agent that cancelled a leadership team meeting, and where the human has to sit in the loop. 🔑 Key Takeaways · Every AI project is a data project with a thin model layer on top. The visible work is the model. The actual work is information retrieval, data modeling, and structuring context so a language model can reach the answer you wanted. Organizations that scope these as model projects underestimate them by an order of magnitude. · Process discipline predicts success better than model selection. Companies that already design, document, and run their business processes rigorously do well, because the technology fills gaps in work they can describe. Companies that cannot describe their own process end up with overlapping agents, messy transitions, and handoffs that never happen. · Multi-agent systems are blocked on governance, not capability. Parallelizing work across sub-agents is well-trodden ground. Agents supervising other agents on real business decisions is not, because agent identity, permission propagation, spend thresholds, and audit trails are still being invented. 🧭 Frameworks Worth Saving Onboard the model like a new employee. The pattern Traey returns to throughout the conversation: · Terminology. Define your organization's specific language. A lane on a highway is not a lane in a transportation contract. · Location. Tell the model where the data actually lives: which systems, which APIs, which warehouse. · Permissions. Give it credentials and role-based access, the same way you would a new hire. · Provenance. Surface how it reached an answer, so the people using it can judge the result. Model choice is a cost decision, not only a capability decision. Route high-value, high-complexity work to frontier models. Route repetitive, well-defined work like document sorting or data entry to smaller, cheaper ones. The deciding question is what optimizing this process actually saves, and whether that justifies the engineering spend. Crawl, walk, run, and most organizations are in the middle. Single-agent workflows and parallelized sub-agents are established. Agents supervising other agents on real business decisions is early, held back by unresolved questions on identity, permissions, and audit trails. 💬 Notable Quotes It's a gigantic data project with a very thin layer of AI model use over the top of it. Traey Hatch A valid result of a proof of concept is negative. Traey Hatch I think that this is the age of the operations person. Traey Hatch 👤 About The Guest Traey Hatch, CEO, New Math Data Traey Hatch is a co-founder of New Math Data and a cloud and data engineering leader focused on AWS, AI, and scalable analytics platforms. He works with organizations to modernize infrastructure, unlock data value, and deploy production-grade AI systems with an emphasis on security, governance, and long-term operability. 🎙️ About The Host Derek Aranda Derek Aranda spent over two decades as a global ...
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    50 分
  • AI ROI in Supply Chain: Autonomy Is an Asset, Not an Expense

    AI ROI in Supply Chain: Autonomy Is an Asset, Not an Expense
    2026/08/13
    Six episodes into Supply ChAInge, the definitions, the leadership, the culture, the data foundation, and the governance are all on the table. This solo episode takes on a question underpinning all of them: why pursue autonomy at all? Derek Aranda borrows Donald Rumsfeld's knowns, known unknowns, and unknown unknowns to map where supply chain value sits, and argues that measuring return through a cost lens alone turns an investment conversation into a headcount conversation. The case he makes is that autonomy is an asset, not an expense. Decision quality and decision velocity compound as you move up the autonomy pyramid, resilience and optionality cover the shocks you cannot forecast, and a fourth bucket of customers, markets, and products opens up once the first three are working. 🎧 Episode Highlights [0:00] A Churchill line frames the moment: not the end, not the beginning of the end, but the end of the beginning. [0:22] The ground covered so far. Definitions, leadership, culture, data, and governance. [1:59] Why the value conversation has to move past cost. [2:28] Channeling Rumsfeld. The knowns, the known unknowns, and the unknown unknowns of supply chain value. [5:02] The standard ROI numerator is just a change in cost profile. [6:36] Where the danger sits. Pilots get locked into showing a cost result fast. [7:13] The implementation hump. Parallel systems, training, and integrators before the cost curve bends. [8:31] Why headcount becomes the easiest lever to pull, and why that narrows the whole opportunity. [9:17] From a P&L mindset to a balance sheet mindset. Autonomy as an asset whose benefit accrues over time. [10:33] Decision quality and decision velocity, worked through a hurricane in the Gulf. [14:00] Better decisions faster. Why the two compound instead of adding. [15:35] Into the unknown unknowns, where optionality and resilience become the payoff. [18:13] The Panama Canal case. High value cargo pays to jump the queue and grain sits further back. [21:03] The fourth bucket. Customers, markets, and products that open up once the first three are working. [23:26] The leadership job. Reorienting what gets measured, what gets rewarded, and who gets credit. 🔑 Key Takeaways · Cost is a necessary lens and an insufficient one. Valuing autonomy purely on cost creates pressure to show savings inside a quarter, and the fastest way to show savings is to remove people. That reflex narrows the opportunity to the one bucket you already knew how to measure. · Autonomy is an asset, so value it on a balance sheet, not a P&L. The benefit accrues over time, and the journey includes false starts. A narrow payback window forces suboptimal choices before the capability has had a chance to compound. · Decision quality and decision velocity compound, and resilience is what covers the rest. Moving up the autonomy pyramid means better decisions made faster against the known unknowns, and a wider surface area of visibility against the shocks nobody forecast. The organizations that capture the most value are the ones that widen what they measure and reward. 🧭 Frameworks Worth Saving Three lenses, four value buckets. Map the investment case against what you can and cannot know. · Knowns produce the cost bucket. Inventory positions, transportation lanes, maintenance spend. Real, measurable, and the only one most business cases capture. · Known unknowns produce the decision bucket. A hurricane will hit somewhere this year. Value shows up as decision quality, the range and consistency of the trade-offs you can make, and decision velocity, how fast new signal reaches those decisions. · Unknown unknowns produce the resilience bucket. Optionality and response capability against events you cannot name in advance. · The fourth bucket is growth. Customers you can win, markets you can enter, products you can grow into once the first three are working in your favor. The test for any business case: what does this do besides cost? How does it help us decide better or faster? How does it help us absorb a shock? 💬 Notable Quotes That cost efficiency mindset, again, is not wrong. It's just not enough. Derek Aranda Autonomy, AI, digital is really an investment, not an expense, not a cost. Autonomy is an asset. And as an asset, the benefit accrues over time. Derek Aranda The opportunity mindset allows us to explore the full spectrum, cost plus, plus, plus. Derek Aranda 🎙️ About The Host Derek Aranda Derek Aranda spent over two decades as a global executive operating across commercial, supply chain, and digital transformation roles at scale. That span across the full value chain shapes his lens: the decisions, incentives, trust, economics, and governance that determine whether technology actually works inside real supply chains. On Supply ChAInge, he pressure-tests the autonomous future and helps leaders shape the framework to build it on their own terms. Stay Connected · https:/...
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    26 分
  • AI Governance in Supply Chain: Rocket Ship, Not a Rulebook

    AI Governance in Supply Chain: Rocket Ship, Not a Rulebook
    2026/07/30
    Most leaders treat AI governance as a leash. Derek Aranda sits down with LeAnne Coulter, founder of BlueWave Supply Chain and a 30-year veteran of designing solutions for Fortune 500 supply chains, to argue the opposite: governance is the rocket ship, not the rulebook. Drawing on a major outsourcing partnership the two worked from opposite sides of the table, the conversation reframes AI governance in supply chain as shared language, a trust framework, and a design discipline rather than a compliance checklist. LeAnne and Derek dig into why the word "pilot" invites pocket vetoes, why AI adoption is really an exercise in onboarding a new team member, and why the biggest return hides in the 30 to 60 percent of hidden work no job description ever mentions. 🎧 Episode Highlights [0:43] Why you don't get to autonomy without governance, and why skipping it gets you chaos instead. [4:15] AI as outsourcing: why bringing on an AI teammate demands the same trust-building as any new hire or partner. [17:33] Pocket vetoes: why the word "pilot" quietly signals a lack of commitment, and what to say instead. [27:40] Whether your governance structures are already archaic, and why decision points designed for 2020 may be the barrier in 2026. [30:39] Why AI breaks processes, and why those breaks were human workarounds all along. [39:35] The hidden 30 to 60 percent: how a 65-person email chain over a shipping document exposes where the time actually goes. [42:00] From roughly 50 percent to over 99 percent: what happened when one company put an agent on carrier visibility, and where the reclaimed capacity went. [43:32] The revenue case nobody makes: the customers you cannot serve today because your people are chasing a truck. 🔑 Key Takeaways Governance is a design conversation, not a control system. It starts with a shared vision and objectives, then establishes the ways of working that let people, and now AI agents, collaborate with confidence. Treated as guardrails rather than restrictions, governance is what lets organizations move fast without going rogue. Language is a governance tool. Words like transformation, integration, and platform mean different things across functions. Getting explicit about shared definitions, and swapping pilot for phase and project for journey, removes the ambiguity that breeds resistance and pocket vetoes. The real prize is hidden work, not headcount. Thirty to 60 percent of time in supply chain roles goes to invisible workarounds, like a 65-person email thread untangling a shipping document. One company put an agent on carrier visibility, moved from roughly 50 percent coverage to over 99 percent, and pointed the reclaimed capacity at OTIF work nobody had ever had time for. That is where the dollars are, and it is a different number than a headcount line. 🧭 Frameworks Worth Saving In the loop, on the loop, out of the loop. The promise sold to executives has been out of the loop: the system decides and the people go away. LeAnne's position is that human in the loop is not a training-wheels phase on the way to that. It becomes the governance structure itself. In the loop means you sit inside the decision. On the loop means you hold oversight of decisions the system makes. Out of the loop is where consistency and institutional knowledge quietly disappear. The word swaps. Pilot becomes phase. Project becomes journey. Pilot and project both carry an end date and an implied exit, and that exit is exactly what gives a reluctant stakeholder the pocket veto. Phase and journey carry commitment, and they tell people the direction is settled even when the route is not. 💬 Notable Quotes "There is not a supply chain silver bullet that is going to take care of your problems." LeAnne Coulter "When I think of pilots... it conveys almost a lack of commitment, because there's an element there that says, 'If I don't like the outcome, I can reserve judgment.'" LeAnne Coulter "It's a rocket ship that you're putting on the back of your people to go to places that we've never been before." Derek Aranda "You don't wanna lose that. You wanna take that curiosity and that problem-solving to then start to look at those process breaks that AI brings forward." LeAnne Coulter 👤 About The Guest LeAnne Coulter is a supply chain executive and technologist with 30 years of experience leading operations, strategy, and digital transformation across Fortune 500 supply chains. Founder of BlueWave Supply Chain, she helps organizations leverage AI and emerging technology to strengthen profitability, agility, and performance. She serves on the Wayne State University Global Supply Chain Management Advisory Board and the FourKites Strategic Advisory Council. https://bluewavesupplychain.com Stay Connected: https://supplychaingepodcast.com https://april12advisors.com Produced by Speakerbox Media.
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    45 分
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